Overview

Dataset statistics

Number of variables44
Number of observations40336
Missing cells509176
Missing cells (%)28.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.5 MiB
Average record size in memory352.0 B

Variable types

Numeric39
Categorical5

Alerts

SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with MAPHigh correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcess and 1 other fieldsHigh correlation
pH is highly correlated with BaseExcessHigh correlation
PaCO2 is highly correlated with HCO3High correlation
AST is highly correlated with Bilirubin_directHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with AST and 1 other fieldsHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
SBP is highly correlated with MAP and 1 other fieldsHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with SBP and 1 other fieldsHigh correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcess and 1 other fieldsHigh correlation
pH is highly correlated with BaseExcessHigh correlation
PaCO2 is highly correlated with HCO3High correlation
BUN is highly correlated with Creatinine and 1 other fieldsHigh correlation
Creatinine is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Phosphate is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with MAPHigh correlation
BaseExcess is highly correlated with HCO3High correlation
HCO3 is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
Unit2 is highly correlated with Unit1High correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Unit1 is highly correlated with Unit2High correlation
DBP has 7411 (18.4%) missing values Missing
EtCO2 has 37120 (92.0%) missing values Missing
BaseExcess has 27126 (67.3%) missing values Missing
HCO3 has 20119 (49.9%) missing values Missing
FiO2 has 22527 (55.8%) missing values Missing
pH has 21401 (53.1%) missing values Missing
PaCO2 has 21980 (54.5%) missing values Missing
SaO2 has 27248 (67.6%) missing values Missing
AST has 25979 (64.4%) missing values Missing
BUN has 2018 (5.0%) missing values Missing
Alkalinephos has 26163 (64.9%) missing values Missing
Calcium has 5339 (13.2%) missing values Missing
Chloride has 18925 (46.9%) missing values Missing
Creatinine has 2049 (5.1%) missing values Missing
Bilirubin_direct has 38279 (94.9%) missing values Missing
Glucose has 1580 (3.9%) missing values Missing
Lactate has 27843 (69.0%) missing values Missing
Magnesium has 4931 (12.2%) missing values Missing
Phosphate has 12015 (29.8%) missing values Missing
Potassium has 1867 (4.6%) missing values Missing
Bilirubin_total has 26088 (64.7%) missing values Missing
TroponinI has 33283 (82.5%) missing values Missing
Hct has 2317 (5.7%) missing values Missing
Hgb has 2448 (6.1%) missing values Missing
PTT has 20098 (49.8%) missing values Missing
WBC has 2625 (6.5%) missing values Missing
Fibrinogen has 35821 (88.8%) missing values Missing
Platelets has 2577 (6.4%) missing values Missing
Unit1 has 15617 (38.7%) missing values Missing
Unit2 has 15617 (38.7%) missing values Missing
PatientID has unique values Unique
BaseExcess has 3002 (7.4%) zeros Zeros
HospAdmTime has 1313 (3.3%) zeros Zeros

Reproduction

Analysis started2021-11-29 10:25:50.547922
Analysis finished2021-11-29 10:26:02.951430
Duration12.4 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

PatientID
Real number (ℝ≥0)

UNIQUE

Distinct40336
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59671.27286
Minimum1
Maximum120000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:02.995555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2017.75
Q110084.75
median20475.5
Q3109916.25
95-th percentile117983.25
Maximum120000
Range119999
Interquartile range (IQR)99831.5

Descriptive statistics

Standard deviation50251.33712
Coefficient of variation (CV)0.842136169
Kurtosis-1.946653503
Mean59671.27286
Median Absolute Deviation (MAD)20307
Skewness0.01560297418
Sum2406900462
Variance2525196883
MonotonicityStrictly increasing
2021-11-29T11:26:03.098132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
1065591
 
< 0.1%
1065521
 
< 0.1%
1065531
 
< 0.1%
1065541
 
< 0.1%
1065551
 
< 0.1%
1065561
 
< 0.1%
1065571
 
< 0.1%
1065581
 
< 0.1%
1065601
 
< 0.1%
Other values (40326)40326
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
1200001
< 0.1%
1199991
< 0.1%
1199981
< 0.1%
1199971
< 0.1%
1199961
< 0.1%
1199951
< 0.1%
1199941
< 0.1%
1199931
< 0.1%
1199921
< 0.1%
1199911
< 0.1%

HR
Real number (ℝ≥0)

Distinct388
Distinct (%)1.0%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean83.26571372
Minimum30
Maximum176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:03.197975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile60
Q172.5
median82
Q393
95-th percentile109
Maximum176
Range146
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation15.08931791
Coefficient of variation (CV)0.1812188623
Kurtosis0.2360797693
Mean83.26571372
Median Absolute Deviation (MAD)10
Skewness0.3493577592
Sum3358189.5
Variance227.687515
MonotonicityNot monotonic
2021-11-29T11:26:03.295035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
801306
 
3.2%
901012
 
2.5%
82905
 
2.2%
84893
 
2.2%
70858
 
2.1%
88837
 
2.1%
78836
 
2.1%
86764
 
1.9%
74762
 
1.9%
76756
 
1.9%
Other values (378)31402
77.9%
ValueCountFrequency (%)
302
< 0.1%
311
< 0.1%
31.51
< 0.1%
332
< 0.1%
341
< 0.1%
362
< 0.1%
372
< 0.1%
37.751
< 0.1%
382
< 0.1%
38.51
< 0.1%
ValueCountFrequency (%)
1761
 
< 0.1%
1631
 
< 0.1%
156.51
 
< 0.1%
1561
 
< 0.1%
1551
 
< 0.1%
1512
< 0.1%
1492
< 0.1%
1473
< 0.1%
1462
< 0.1%
1441
 
< 0.1%

O2Sat
Real number (ℝ≥0)

Distinct95
Distinct (%)0.2%
Missing18
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean97.44818071
Minimum27
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:03.398751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile94
Q196
median98
Q399
95-th percentile100
Maximum100
Range73
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.194164972
Coefficient of variation (CV)0.02251622304
Kurtosis85.40926719
Mean97.44818071
Median Absolute Deviation (MAD)1
Skewness-4.711538543
Sum3928915.75
Variance4.814359924
MonotonicityNot monotonic
2021-11-29T11:26:03.571262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
986708
16.6%
1006494
16.1%
976190
15.3%
995504
13.6%
965040
12.5%
952758
6.8%
941229
 
3.0%
97.5879
 
2.2%
98.5761
 
1.9%
96.5729
 
1.8%
Other values (85)4026
10.0%
ValueCountFrequency (%)
271
< 0.1%
34.251
< 0.1%
491
< 0.1%
51.751
< 0.1%
55.51
< 0.1%
57.51
< 0.1%
581
< 0.1%
62.251
< 0.1%
631
< 0.1%
64.251
< 0.1%
ValueCountFrequency (%)
1006494
16.1%
99.75144
 
0.4%
99.5548
 
1.4%
99.25155
 
0.4%
995504
13.6%
98.75166
 
0.4%
98.5761
 
1.9%
98.25161
 
0.4%
986708
16.6%
97.75170
 
0.4%

Temp
Real number (ℝ≥0)

Distinct617
Distinct (%)1.5%
Missing284
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean36.86648719
Minimum30.5
Maximum40.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:03.673924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30.5
5-th percentile36
Q136.5
median36.83
Q337.22
95-th percentile37.805
Maximum40.4
Range9.9
Interquartile range (IQR)0.72

Descriptive statistics

Standard deviation0.5789839904
Coefficient of variation (CV)0.01570488632
Kurtosis2.985685407
Mean36.86648719
Median Absolute Deviation (MAD)0.37
Skewness-0.1533164972
Sum1476576.545
Variance0.3352224611
MonotonicityNot monotonic
2021-11-29T11:26:03.770065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
371883
 
4.7%
36.51714
 
4.2%
36.61434
 
3.6%
36.81386
 
3.4%
36.71356
 
3.4%
36.91000
 
2.5%
36.4947
 
2.3%
37.1929
 
2.3%
37.2892
 
2.2%
37.5841
 
2.1%
Other values (607)27670
68.6%
ValueCountFrequency (%)
30.51
< 0.1%
31.41
< 0.1%
31.671
< 0.1%
32.12
< 0.1%
32.351
< 0.1%
32.521
< 0.1%
32.641
< 0.1%
32.82
< 0.1%
32.91
< 0.1%
32.952
< 0.1%
ValueCountFrequency (%)
40.41
< 0.1%
40.11
< 0.1%
40.031
< 0.1%
402
< 0.1%
39.71
< 0.1%
39.611
< 0.1%
39.52
< 0.1%
39.41
< 0.1%
39.351
< 0.1%
39.332
< 0.1%

SBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct599
Distinct (%)1.5%
Missing282
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean122.958586
Minimum35
Maximum216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:03.867966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile97
Q1109.5
median121
Q3135
95-th percentile156
Maximum216
Range181
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation18.39552154
Coefficient of variation (CV)0.1496074584
Kurtosis0.1819770444
Mean122.958586
Median Absolute Deviation (MAD)12.5
Skewness0.4807569725
Sum4924983.205
Variance338.3952128
MonotonicityNot monotonic
2021-11-29T11:26:03.965990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116674
 
1.7%
112670
 
1.7%
110666
 
1.7%
115642
 
1.6%
114638
 
1.6%
118638
 
1.6%
113625
 
1.5%
117624
 
1.5%
119623
 
1.5%
121610
 
1.5%
Other values (589)33644
83.4%
ValueCountFrequency (%)
351
< 0.1%
361
< 0.1%
451
< 0.1%
511
< 0.1%
51.51
< 0.1%
54.51
< 0.1%
551
< 0.1%
562
< 0.1%
56.51
< 0.1%
571
< 0.1%
ValueCountFrequency (%)
2161
< 0.1%
2071
< 0.1%
2061
< 0.1%
2031
< 0.1%
2021
< 0.1%
199.51
< 0.1%
1981
< 0.1%
1971
< 0.1%
196.51
< 0.1%
195.51
< 0.1%

MAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct926
Distinct (%)2.3%
Missing104
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean82.01537234
Minimum22
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:04.068071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile64.5
Q173
median80
Q390
95-th percentile106
Maximum144
Range122
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.85974789
Coefficient of variation (CV)0.1567967995
Kurtosis0.6423404008
Mean82.01537234
Median Absolute Deviation (MAD)8
Skewness0.684619077
Sum3299642.46
Variance165.3731159
MonotonicityNot monotonic
2021-11-29T11:26:04.171054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76984
 
2.4%
78959
 
2.4%
74923
 
2.3%
72920
 
2.3%
80899
 
2.2%
77880
 
2.2%
82878
 
2.2%
75869
 
2.2%
73818
 
2.0%
79817
 
2.0%
Other values (916)31285
77.6%
ValueCountFrequency (%)
221
< 0.1%
27.251
< 0.1%
301
< 0.1%
331
< 0.1%
341
< 0.1%
34.51
< 0.1%
35.831
< 0.1%
411
< 0.1%
422
< 0.1%
43.251
< 0.1%
ValueCountFrequency (%)
1441
 
< 0.1%
143.51
 
< 0.1%
1431
 
< 0.1%
1421
 
< 0.1%
1411
 
< 0.1%
1404
< 0.1%
139.51
 
< 0.1%
139.251
 
< 0.1%
1391
 
< 0.1%
138.52
< 0.1%

DBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct374
Distinct (%)1.1%
Missing7411
Missing (%)18.4%
Infinite0
Infinite (%)0.0%
Mean63.43786849
Minimum22
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:04.274545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile47.5
Q156
median62
Q370
95-th percentile84
Maximum134
Range112
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.15759694
Coefficient of variation (CV)0.1758822799
Kurtosis0.5913272709
Mean63.43786849
Median Absolute Deviation (MAD)7
Skewness0.582061897
Sum2088691.82
Variance124.4919695
MonotonicityNot monotonic
2021-11-29T11:26:04.370867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
581062
 
2.6%
601005
 
2.5%
56998
 
2.5%
62944
 
2.3%
59934
 
2.3%
57885
 
2.2%
64881
 
2.2%
63879
 
2.2%
61856
 
2.1%
55850
 
2.1%
Other values (364)23631
58.6%
(Missing)7411
 
18.4%
ValueCountFrequency (%)
221
 
< 0.1%
241
 
< 0.1%
261
 
< 0.1%
27.52
 
< 0.1%
282
 
< 0.1%
28.51
 
< 0.1%
298
< 0.1%
301
 
< 0.1%
311
 
< 0.1%
31.51
 
< 0.1%
ValueCountFrequency (%)
1341
 
< 0.1%
1311
 
< 0.1%
1171
 
< 0.1%
1161
 
< 0.1%
114.751
 
< 0.1%
1131
 
< 0.1%
1124
< 0.1%
1111
 
< 0.1%
110.751
 
< 0.1%
110.51
 
< 0.1%

Resp
Real number (ℝ≥0)

Distinct176
Distinct (%)0.4%
Missing71
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean18.3280719
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:04.471931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q116
median18
Q320
95-th percentile24.5
Maximum56
Range55
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.575971875
Coefficient of variation (CV)0.1951090052
Kurtosis3.256119895
Mean18.3280719
Median Absolute Deviation (MAD)2
Skewness0.7426021231
Sum737979.815
Variance12.78757485
MonotonicityNot monotonic
2021-11-29T11:26:04.563862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185997
14.9%
164624
11.5%
204316
 
10.7%
173062
 
7.6%
192523
 
6.3%
152267
 
5.6%
141879
 
4.7%
221807
 
4.5%
211652
 
4.1%
24820
 
2.0%
Other values (166)11318
28.1%
ValueCountFrequency (%)
18
 
< 0.1%
1.53
 
< 0.1%
224
0.1%
2.53
 
< 0.1%
312
< 0.1%
3.253
 
< 0.1%
3.54
 
< 0.1%
3.751
 
< 0.1%
411
< 0.1%
4.53
 
< 0.1%
ValueCountFrequency (%)
562
 
< 0.1%
51.51
 
< 0.1%
421
 
< 0.1%
41.51
 
< 0.1%
404
< 0.1%
391
 
< 0.1%
38.52
 
< 0.1%
383
< 0.1%
37.51
 
< 0.1%
375
< 0.1%

EtCO2
Real number (ℝ≥0)

MISSING

Distinct173
Distinct (%)5.4%
Missing37120
Missing (%)92.0%
Infinite0
Infinite (%)0.0%
Mean33.04485386
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:04.664874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile17.5
Q128
median33
Q337
95-th percentile43.5
Maximum100
Range90
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.32476853
Coefficient of variation (CV)0.3124470931
Kurtosis16.13061293
Mean33.04485386
Median Absolute Deviation (MAD)4.5
Skewness2.621801772
Sum106272.25
Variance106.6008452
MonotonicityNot monotonic
2021-11-29T11:26:04.760174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35137
 
0.3%
34136
 
0.3%
33136
 
0.3%
32131
 
0.3%
30124
 
0.3%
36112
 
0.3%
29109
 
0.3%
3998
 
0.2%
2896
 
0.2%
3795
 
0.2%
Other values (163)2042
 
5.1%
(Missing)37120
92.0%
ValueCountFrequency (%)
108
< 0.1%
10.56
 
< 0.1%
1110
< 0.1%
11.254
 
< 0.1%
11.56
 
< 0.1%
1215
< 0.1%
12.55
 
< 0.1%
1310
< 0.1%
13.252
 
< 0.1%
13.56
 
< 0.1%
ValueCountFrequency (%)
1007
< 0.1%
994
< 0.1%
98.51
 
< 0.1%
986
< 0.1%
97.52
 
< 0.1%
977
< 0.1%
962
 
< 0.1%
951
 
< 0.1%
942
 
< 0.1%
932
 
< 0.1%

BaseExcess
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct360
Distinct (%)2.7%
Missing27126
Missing (%)67.3%
Infinite0
Infinite (%)0.0%
Mean-0.4412036336
Minimum-25.5
Maximum25
Zeros3002
Zeros (%)7.4%
Negative5988
Negative (%)14.8%
Memory size315.2 KiB
2021-11-29T11:26:04.929502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-25.5
5-th percentile-6.5
Q1-2
median0
Q31
95-th percentile6
Maximum25
Range50.5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.871915437
Coefficient of variation (CV)-8.775801335
Kurtosis4.801227958
Mean-0.4412036336
Median Absolute Deviation (MAD)2
Skewness-0.1101170191
Sum-5828.3
Variance14.99172915
MonotonicityNot monotonic
2021-11-29T11:26:05.026079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03002
 
7.4%
-11169
 
2.9%
-2915
 
2.3%
1877
 
2.2%
-3722
 
1.8%
2669
 
1.7%
3482
 
1.2%
-4477
 
1.2%
-0.5357
 
0.9%
4344
 
0.9%
Other values (350)4196
 
10.4%
(Missing)27126
67.3%
ValueCountFrequency (%)
-25.51
< 0.1%
-251
< 0.1%
-24.51
< 0.1%
-242
< 0.1%
-231
< 0.1%
-22.251
< 0.1%
-22.11
< 0.1%
-21.51
< 0.1%
-212
< 0.1%
-20.51
< 0.1%
ValueCountFrequency (%)
251
 
< 0.1%
241
 
< 0.1%
213
 
< 0.1%
201
 
< 0.1%
195
< 0.1%
189
< 0.1%
17.51
 
< 0.1%
175
< 0.1%
16.51
 
< 0.1%
1611
< 0.1%

HCO3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct293
Distinct (%)1.4%
Missing20119
Missing (%)49.9%
Infinite0
Infinite (%)0.0%
Mean24.38575333
Minimum5
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:05.121578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile18
Q122
median24
Q326.5
95-th percentile31
Maximum55
Range50
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.923996022
Coefficient of variation (CV)0.1609134633
Kurtosis3.06047542
Mean24.38575333
Median Absolute Deviation (MAD)2
Skewness0.3935845706
Sum493006.775
Variance15.39774478
MonotonicityNot monotonic
2021-11-29T11:26:05.217235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
252058
 
5.1%
241975
 
4.9%
231786
 
4.4%
261672
 
4.1%
221411
 
3.5%
271248
 
3.1%
21922
 
2.3%
28833
 
2.1%
20684
 
1.7%
24.5591
 
1.5%
Other values (283)7037
 
17.4%
(Missing)20119
49.9%
ValueCountFrequency (%)
51
 
< 0.1%
61
 
< 0.1%
6.52
 
< 0.1%
73
 
< 0.1%
7.71
 
< 0.1%
87
< 0.1%
8.52
 
< 0.1%
98
< 0.1%
9.52
 
< 0.1%
108
< 0.1%
ValueCountFrequency (%)
551
 
< 0.1%
511
 
< 0.1%
501
 
< 0.1%
491
 
< 0.1%
481
 
< 0.1%
47.51
 
< 0.1%
471
 
< 0.1%
46.51
 
< 0.1%
463
< 0.1%
45.51
 
< 0.1%

FiO2
Real number (ℝ≥0)

MISSING

Distinct158
Distinct (%)0.9%
Missing22527
Missing (%)55.8%
Infinite0
Infinite (%)0.0%
Mean0.4979970801
Minimum0
Maximum10
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:05.314438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28
Q10.4
median0.5
Q30.5
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1874790728
Coefficient of variation (CV)0.3764662089
Kurtosis372.3091547
Mean0.4979970801
Median Absolute Deviation (MAD)0.1
Skewness8.457779933
Sum8868.83
Variance0.03514840273
MonotonicityNot monotonic
2021-11-29T11:26:05.411818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.45223
 
12.9%
0.55214
 
12.9%
0.61051
 
2.6%
1908
 
2.3%
0.7767
 
1.9%
0.45680
 
1.7%
0.21646
 
1.6%
0.35454
 
1.1%
0.3385
 
1.0%
0.55353
 
0.9%
Other values (148)2128
 
5.3%
(Missing)22527
55.8%
ValueCountFrequency (%)
02
 
< 0.1%
0.022
 
< 0.1%
0.034
< 0.1%
0.049
< 0.1%
0.057
< 0.1%
0.065
< 0.1%
0.081
 
< 0.1%
0.111
 
< 0.1%
0.1151
 
< 0.1%
0.151
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
24
 
< 0.1%
1.61
 
< 0.1%
1.52
 
< 0.1%
1.21
 
< 0.1%
1908
2.3%
0.9951
 
< 0.1%
0.995
 
< 0.1%
0.985
 
< 0.1%
0.9753
 
< 0.1%

pH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct167
Distinct (%)0.9%
Missing21401
Missing (%)53.1%
Infinite0
Infinite (%)0.0%
Mean7.383056245
Minimum6.63
Maximum7.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:05.511655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.63
5-th percentile7.28
Q17.35
median7.385
Q37.42
95-th percentile7.48
Maximum7.73
Range1.1
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.06272471385
Coefficient of variation (CV)0.008495765408
Kurtosis6.641986026
Mean7.383056245
Median Absolute Deviation (MAD)0.035
Skewness-1.087248194
Sum139798.17
Variance0.003934389727
MonotonicityNot monotonic
2021-11-29T11:26:05.610048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.41227
 
3.0%
7.381176
 
2.9%
7.371072
 
2.7%
7.39983
 
2.4%
7.41971
 
2.4%
7.42949
 
2.4%
7.36889
 
2.2%
7.35866
 
2.1%
7.43746
 
1.8%
7.34715
 
1.8%
Other values (157)9341
23.2%
(Missing)21401
53.1%
ValueCountFrequency (%)
6.631
< 0.1%
6.651
< 0.1%
6.781
< 0.1%
6.811
< 0.1%
6.871
< 0.1%
6.91
< 0.1%
6.921
< 0.1%
6.941
< 0.1%
6.942
< 0.1%
6.962
< 0.1%
ValueCountFrequency (%)
7.731
 
< 0.1%
7.6951
 
< 0.1%
7.661
 
< 0.1%
7.631
 
< 0.1%
7.6151
 
< 0.1%
7.611
 
< 0.1%
7.64
< 0.1%
7.5951
 
< 0.1%
7.595
< 0.1%
7.5852
 
< 0.1%

PaCO2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct605
Distinct (%)3.3%
Missing21980
Missing (%)54.5%
Infinite0
Infinite (%)0.0%
Mean40.74534621
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:05.713870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q136
median40
Q344
95-th percentile54
Maximum100
Range90
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.350196857
Coefficient of variation (CV)0.2049362107
Kurtosis7.130111035
Mean40.74534621
Median Absolute Deviation (MAD)4
Skewness1.778301993
Sum747921.575
Variance69.72578756
MonotonicityNot monotonic
2021-11-29T11:26:05.806350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40954
 
2.4%
38917
 
2.3%
39869
 
2.2%
41845
 
2.1%
42816
 
2.0%
37803
 
2.0%
36759
 
1.9%
43694
 
1.7%
44649
 
1.6%
35614
 
1.5%
Other values (595)10436
25.9%
(Missing)21980
54.5%
ValueCountFrequency (%)
101
 
< 0.1%
121
 
< 0.1%
153
< 0.1%
15.31
 
< 0.1%
15.52
 
< 0.1%
166
< 0.1%
16.71
 
< 0.1%
174
< 0.1%
186
< 0.1%
18.51
 
< 0.1%
ValueCountFrequency (%)
1001
 
< 0.1%
991
 
< 0.1%
983
< 0.1%
96.51
 
< 0.1%
951
 
< 0.1%
942
< 0.1%
93.51
 
< 0.1%
93.41
 
< 0.1%
932
< 0.1%
92.52
< 0.1%

SaO2
Real number (ℝ≥0)

MISSING

Distinct483
Distinct (%)3.7%
Missing27248
Missing (%)67.6%
Infinite0
Infinite (%)0.0%
Mean94.46866595
Minimum29.5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:05.901970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum29.5
5-th percentile74.5
Q195.2
median97
Q398
95-th percentile99.15
Maximum100
Range70.5
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation7.930001499
Coefficient of variation (CV)0.08394319343
Kurtosis10.2215122
Mean94.46866595
Median Absolute Deviation (MAD)1.05
Skewness-3.091524801
Sum1236405.9
Variance62.88492377
MonotonicityNot monotonic
2021-11-29T11:26:06.000041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
982154
 
5.3%
971442
 
3.6%
96753
 
1.9%
99565
 
1.4%
97.5427
 
1.1%
95370
 
0.9%
98.5299
 
0.7%
96.5294
 
0.7%
94226
 
0.6%
95.5152
 
0.4%
Other values (473)6406
 
15.9%
(Missing)27248
67.6%
ValueCountFrequency (%)
29.51
< 0.1%
301
< 0.1%
311
< 0.1%
31.51
< 0.1%
401
< 0.1%
40.51
< 0.1%
41.51
< 0.1%
422
< 0.1%
431
< 0.1%
442
< 0.1%
ValueCountFrequency (%)
10022
 
0.1%
99.931
0.1%
99.852
 
< 0.1%
99.82
 
< 0.1%
99.843
0.1%
99.754
 
< 0.1%
99.755
0.1%
99.73
 
< 0.1%
99.6512
 
< 0.1%
99.6252
 
< 0.1%

AST
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1292
Distinct (%)9.0%
Missing25979
Missing (%)64.4%
Infinite0
Infinite (%)0.0%
Mean133.3110678
Minimum3
Maximum9264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:06.104248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile13
Q120
median33
Q368
95-th percentile422.8
Maximum9264
Range9261
Interquartile range (IQR)48

Descriptive statistics

Standard deviation504.710739
Coefficient of variation (CV)3.785962767
Kurtosis114.7879558
Mean133.3110678
Median Absolute Deviation (MAD)16
Skewness9.704061792
Sum1913947
Variance254732.9301
MonotonicityNot monotonic
2021-11-29T11:26:06.200294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18398
 
1.0%
19396
 
1.0%
17384
 
1.0%
21367
 
0.9%
20366
 
0.9%
16357
 
0.9%
24353
 
0.9%
22344
 
0.9%
15332
 
0.8%
14304
 
0.8%
Other values (1282)10756
26.7%
(Missing)25979
64.4%
ValueCountFrequency (%)
32
 
< 0.1%
41
 
< 0.1%
53
 
< 0.1%
5.51
 
< 0.1%
610
 
< 0.1%
6.52
 
< 0.1%
712
< 0.1%
825
0.1%
8.252
 
< 0.1%
8.59
 
< 0.1%
ValueCountFrequency (%)
92641
< 0.1%
92101
< 0.1%
85911
< 0.1%
84721
< 0.1%
79381
< 0.1%
79061
< 0.1%
77511
< 0.1%
76991
< 0.1%
76341
< 0.1%
75051
< 0.1%

BUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct338
Distinct (%)0.9%
Missing2018
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean21.95840075
Minimum1
Maximum232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:06.369885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.5
Q111
median16
Q325.5
95-th percentile58
Maximum232
Range231
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation17.97225517
Coefficient of variation (CV)0.8184683106
Kurtosis10.50480026
Mean21.95840075
Median Absolute Deviation (MAD)6
Skewness2.714189985
Sum841402
Variance323.0019558
MonotonicityNot monotonic
2021-11-29T11:26:06.466864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131628
 
4.0%
121604
 
4.0%
141578
 
3.9%
111561
 
3.9%
101451
 
3.6%
151422
 
3.5%
161355
 
3.4%
171240
 
3.1%
91237
 
3.1%
181122
 
2.8%
Other values (328)24120
59.8%
(Missing)2018
 
5.0%
ValueCountFrequency (%)
113
 
< 0.1%
1.252
 
< 0.1%
1.58
 
< 0.1%
236
 
0.1%
2.513
 
< 0.1%
3128
0.3%
3.548
 
0.1%
3.751
 
< 0.1%
4248
0.6%
4.251
 
< 0.1%
ValueCountFrequency (%)
2321
< 0.1%
202.51
< 0.1%
2001
< 0.1%
187.51
< 0.1%
1821
< 0.1%
179.51
< 0.1%
1771
< 0.1%
175.51
< 0.1%
1721
< 0.1%
1702
< 0.1%

Alkalinephos
Real number (ℝ≥0)

MISSING

Distinct842
Distinct (%)5.9%
Missing26163
Missing (%)64.9%
Infinite0
Infinite (%)0.0%
Mean96.69163198
Minimum7
Maximum3726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:06.567401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile36
Q154
median72
Q3102.5
95-th percentile232.2
Maximum3726
Range3719
Interquartile range (IQR)48.5

Descriptive statistics

Standard deviation104.6371549
Coefficient of variation (CV)1.082173843
Kurtosis190.9283797
Mean96.69163198
Median Absolute Deviation (MAD)22
Skewness9.706396917
Sum1370410.5
Variance10948.93419
MonotonicityNot monotonic
2021-11-29T11:26:06.665856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53194
 
0.5%
55193
 
0.5%
59190
 
0.5%
69189
 
0.5%
52187
 
0.5%
58187
 
0.5%
61183
 
0.5%
49179
 
0.4%
54177
 
0.4%
60173
 
0.4%
Other values (832)12321
30.5%
(Missing)26163
64.9%
ValueCountFrequency (%)
71
 
< 0.1%
112
 
< 0.1%
121
 
< 0.1%
131
 
< 0.1%
141
 
< 0.1%
152
 
< 0.1%
162
 
< 0.1%
171
 
< 0.1%
186
< 0.1%
196
< 0.1%
ValueCountFrequency (%)
37261
< 0.1%
25281
< 0.1%
23321
< 0.1%
2145.51
< 0.1%
20201
< 0.1%
16691
< 0.1%
16502
< 0.1%
15001
< 0.1%
14511
< 0.1%
14371
< 0.1%

Calcium
Real number (ℝ≥0)

MISSING

Distinct1031
Distinct (%)2.9%
Missing5339
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean8.054353087
Minimum1.01
Maximum27.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:06.769286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.01
5-th percentile4.745
Q17.85
median8.3
Q38.8
95-th percentile9.4
Maximum27.45
Range26.44
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation1.590536315
Coefficient of variation (CV)0.197475365
Kurtosis11.09969789
Mean8.054353087
Median Absolute Deviation (MAD)0.45
Skewness-2.157125211
Sum281878.195
Variance2.529805771
MonotonicityNot monotonic
2021-11-29T11:26:06.867040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.31700
 
4.2%
8.51659
 
4.1%
8.21586
 
3.9%
8.41536
 
3.8%
8.61489
 
3.7%
8.71442
 
3.6%
8.11432
 
3.6%
8.81371
 
3.4%
81308
 
3.2%
7.91123
 
2.8%
Other values (1021)20351
50.5%
(Missing)5339
 
13.2%
ValueCountFrequency (%)
1.011
 
< 0.1%
1.043
 
< 0.1%
1.051
 
< 0.1%
1.0551
 
< 0.1%
1.061
 
< 0.1%
1.076
< 0.1%
1.0752
 
< 0.1%
1.086
< 0.1%
1.0852
 
< 0.1%
1.0913
< 0.1%
ValueCountFrequency (%)
27.451
 
< 0.1%
25.21
 
< 0.1%
23.71
 
< 0.1%
19.11
 
< 0.1%
18.82
< 0.1%
18.63
< 0.1%
18.31
 
< 0.1%
18.23
< 0.1%
18.11
 
< 0.1%
182
< 0.1%

Chloride
Real number (ℝ≥0)

MISSING

Distinct127
Distinct (%)0.6%
Missing18925
Missing (%)46.9%
Infinite0
Infinite (%)0.0%
Mean105.580496
Minimum67.5
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:06.964252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum67.5
5-th percentile97
Q1103
median106
Q3109
95-th percentile113
Maximum139
Range71.5
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.180103495
Coefficient of variation (CV)0.04906307216
Kurtosis1.939118047
Mean105.580496
Median Absolute Deviation (MAD)3
Skewness-0.2843858574
Sum2260584
Variance26.83347222
MonotonicityNot monotonic
2021-11-29T11:26:07.063478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1061536
 
3.8%
1071517
 
3.8%
1051453
 
3.6%
1081340
 
3.3%
1041248
 
3.1%
1091184
 
2.9%
1031118
 
2.8%
110938
 
2.3%
102891
 
2.2%
101745
 
1.8%
Other values (117)9441
23.4%
(Missing)18925
46.9%
ValueCountFrequency (%)
67.51
 
< 0.1%
701
 
< 0.1%
731
 
< 0.1%
742
 
< 0.1%
792
 
< 0.1%
801
 
< 0.1%
815
< 0.1%
823
< 0.1%
82.52
 
< 0.1%
834
< 0.1%
ValueCountFrequency (%)
1391
 
< 0.1%
1361
 
< 0.1%
1341
 
< 0.1%
1331
 
< 0.1%
132.51
 
< 0.1%
1313
< 0.1%
130.51
 
< 0.1%
1292
 
< 0.1%
128.51
 
< 0.1%
1286
< 0.1%

Creatinine
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1924
Distinct (%)5.0%
Missing2049
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean1.429553504
Minimum0.1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:07.166004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q10.7
median0.9
Q31.3
95-th percentile4.747
Maximum28
Range27.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation1.771999077
Coefficient of variation (CV)1.239547224
Kurtosis29.83916002
Mean1.429553504
Median Absolute Deviation (MAD)0.25
Skewness4.697239902
Sum54733.315
Variance3.139980728
MonotonicityNot monotonic
2021-11-29T11:26:07.259293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.72232
 
5.5%
0.82206
 
5.5%
0.91841
 
4.6%
0.61762
 
4.4%
11357
 
3.4%
0.51152
 
2.9%
1.1968
 
2.4%
0.75781
 
1.9%
1.2616
 
1.5%
0.85519
 
1.3%
Other values (1914)24853
61.6%
(Missing)2049
 
5.1%
ValueCountFrequency (%)
0.17
 
< 0.1%
0.153
 
< 0.1%
0.222
0.1%
0.2051
 
< 0.1%
0.221
 
< 0.1%
0.2351
 
< 0.1%
0.241
 
< 0.1%
0.2511
< 0.1%
0.271
 
< 0.1%
0.281
 
< 0.1%
ValueCountFrequency (%)
281
< 0.1%
25.331
< 0.1%
252
< 0.1%
23.831
< 0.1%
23.8251
< 0.1%
23.4951
< 0.1%
23.4851
< 0.1%
23.371
< 0.1%
21.311
< 0.1%
21.181
< 0.1%

Bilirubin_direct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct277
Distinct (%)13.5%
Missing38279
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean1.260162859
Minimum0.01
Maximum37.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:07.359964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.1
Q10.1
median0.3
Q30.95
95-th percentile5.554
Maximum37.5
Range37.49
Interquartile range (IQR)0.85

Descriptive statistics

Standard deviation3.040793452
Coefficient of variation (CV)2.413016247
Kurtosis39.08633833
Mean1.260162859
Median Absolute Deviation (MAD)0.2
Skewness5.506693612
Sum2592.155
Variance9.246424815
MonotonicityNot monotonic
2021-11-29T11:26:07.455985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1475
 
1.2%
0.2318
 
0.8%
0.3150
 
0.4%
0.4126
 
0.3%
0.560
 
0.1%
0.655
 
0.1%
0.735
 
0.1%
133
 
0.1%
0.833
 
0.1%
1.124
 
0.1%
Other values (267)748
 
1.9%
(Missing)38279
94.9%
ValueCountFrequency (%)
0.015
< 0.1%
0.024
< 0.1%
0.035
< 0.1%
0.044
< 0.1%
0.051
 
< 0.1%
0.054
< 0.1%
0.066
< 0.1%
0.061
 
< 0.1%
0.077
< 0.1%
0.084
< 0.1%
ValueCountFrequency (%)
37.51
< 0.1%
351
< 0.1%
301
< 0.1%
25.951
< 0.1%
25.21
< 0.1%
23.31
< 0.1%
22.21
< 0.1%
21.651
< 0.1%
21.3351
< 0.1%
21.21
< 0.1%

Glucose
Real number (ℝ≥0)

MISSING

Distinct864
Distinct (%)2.2%
Missing1580
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean129.3001933
Minimum19
Maximum755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:07.557622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile87
Q1106
median122.5
Q3142
95-th percentile198
Maximum755
Range736
Interquartile range (IQR)36

Descriptive statistics

Standard deviation37.39983655
Coefficient of variation (CV)0.2892481102
Kurtosis13.562674
Mean129.3001933
Median Absolute Deviation (MAD)17.5
Skewness2.404663485
Sum5011158.29
Variance1398.747774
MonotonicityNot monotonic
2021-11-29T11:26:07.650566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
118482
 
1.2%
119473
 
1.2%
121471
 
1.2%
126449
 
1.1%
114447
 
1.1%
116447
 
1.1%
112444
 
1.1%
108443
 
1.1%
127442
 
1.1%
109440
 
1.1%
Other values (854)34218
84.8%
(Missing)1580
 
3.9%
ValueCountFrequency (%)
191
< 0.1%
311
< 0.1%
382
< 0.1%
401
< 0.1%
411
< 0.1%
422
< 0.1%
441
< 0.1%
462
< 0.1%
472
< 0.1%
481
< 0.1%
ValueCountFrequency (%)
7551
< 0.1%
671.51
< 0.1%
6661
< 0.1%
5631
< 0.1%
5421
< 0.1%
5311
< 0.1%
522.51
< 0.1%
5011
< 0.1%
482.51
< 0.1%
4721
< 0.1%

Lactate
Real number (ℝ≥0)

MISSING

Distinct1250
Distinct (%)10.0%
Missing27843
Missing (%)69.0%
Infinite0
Infinite (%)0.0%
Mean2.1191071
Minimum0.3
Maximum26.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:07.824011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.8
Q11.2
median1.65
Q32.4
95-th percentile4.9
Maximum26.95
Range26.65
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.751489508
Coefficient of variation (CV)0.8265224101
Kurtosis30.98606726
Mean2.1191071
Median Absolute Deviation (MAD)0.55
Skewness4.477343324
Sum26474.005
Variance3.067715495
MonotonicityNot monotonic
2021-11-29T11:26:07.924332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1429
 
1.1%
1.2426
 
1.1%
1.4409
 
1.0%
1.3402
 
1.0%
1.1402
 
1.0%
0.9357
 
0.9%
1.5351
 
0.9%
1.6328
 
0.8%
1.7290
 
0.7%
1.8274
 
0.7%
Other values (1240)8825
 
21.9%
(Missing)27843
69.0%
ValueCountFrequency (%)
0.33
 
< 0.1%
0.371
 
< 0.1%
0.44
 
< 0.1%
0.524
 
0.1%
0.541
 
< 0.1%
0.559
 
< 0.1%
0.562
 
< 0.1%
0.574
 
< 0.1%
0.591
 
< 0.1%
0.673
0.2%
ValueCountFrequency (%)
26.951
< 0.1%
24.851
< 0.1%
22.31
< 0.1%
22.251
< 0.1%
19.91
< 0.1%
19.3451
< 0.1%
19.31
< 0.1%
19.121
< 0.1%
192
< 0.1%
18.21
< 0.1%

Magnesium
Real number (ℝ≥0)

MISSING

Distinct133
Distinct (%)0.4%
Missing4931
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean2.01881175
Minimum0.5
Maximum8.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:08.023421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.6
Q11.8
median2
Q32.2
95-th percentile2.5
Maximum8.4
Range7.9
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.3221540836
Coefficient of variation (CV)0.1595760891
Kurtosis21.86540068
Mean2.01881175
Median Absolute Deviation (MAD)0.2
Skewness2.146071134
Sum71476.03
Variance0.1037832536
MonotonicityNot monotonic
2021-11-29T11:26:08.121016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24573
11.3%
1.94107
10.2%
2.13616
 
9.0%
1.82976
 
7.4%
2.22690
 
6.7%
1.71973
 
4.9%
2.31811
 
4.5%
1.61269
 
3.1%
2.051180
 
2.9%
1.851060
 
2.6%
Other values (123)10150
25.2%
(Missing)4931
12.2%
ValueCountFrequency (%)
0.51
 
< 0.1%
0.651
 
< 0.1%
0.82
 
< 0.1%
0.94
 
< 0.1%
115
 
< 0.1%
1.052
 
< 0.1%
1.131
0.1%
1.153
 
< 0.1%
1.266
0.2%
1.23
 
< 0.1%
ValueCountFrequency (%)
8.41
< 0.1%
8.21
< 0.1%
7.11
< 0.1%
6.91
< 0.1%
6.61
< 0.1%
6.51
< 0.1%
6.351
< 0.1%
6.22
< 0.1%
5.71
< 0.1%
5.41
< 0.1%

Phosphate
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct324
Distinct (%)1.1%
Missing12015
Missing (%)29.8%
Infinite0
Infinite (%)0.0%
Mean3.536566505
Minimum0.45
Maximum14.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:08.225720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile2
Q12.8
median3.35
Q34
95-th percentile5.8
Maximum14.7
Range14.25
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.226212925
Coefficient of variation (CV)0.3467241243
Kurtosis6.302789728
Mean3.536566505
Median Absolute Deviation (MAD)0.65
Skewness1.751047164
Sum100159.1
Variance1.503598137
MonotonicityNot monotonic
2021-11-29T11:26:08.322584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.21004
 
2.5%
3980
 
2.4%
3.3969
 
2.4%
3.5954
 
2.4%
3.4910
 
2.3%
3.1897
 
2.2%
2.8895
 
2.2%
2.9849
 
2.1%
3.7815
 
2.0%
2.7800
 
2.0%
Other values (314)19248
47.7%
(Missing)12015
29.8%
ValueCountFrequency (%)
0.451
 
< 0.1%
0.52
 
< 0.1%
0.65
< 0.1%
0.651
 
< 0.1%
0.73
 
< 0.1%
0.751
 
< 0.1%
0.89
< 0.1%
0.853
 
< 0.1%
0.99
< 0.1%
0.951
 
< 0.1%
ValueCountFrequency (%)
14.71
< 0.1%
14.351
< 0.1%
13.651
< 0.1%
13.31
< 0.1%
13.051
< 0.1%
131
< 0.1%
12.91
< 0.1%
12.651
< 0.1%
12.61
< 0.1%
12.51
< 0.1%

Potassium
Real number (ℝ≥0)

MISSING

Distinct466
Distinct (%)1.2%
Missing1867
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean4.0859817
Minimum1.575
Maximum9.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:08.426379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.575
5-th percentile3.4
Q13.8
median4
Q34.31
95-th percentile4.95
Maximum9.8
Range8.225
Interquartile range (IQR)0.51

Descriptive statistics

Standard deviation0.495655428
Coefficient of variation (CV)0.121306326
Kurtosis4.605728449
Mean4.0859817
Median Absolute Deviation (MAD)0.3
Skewness1.074086067
Sum157183.63
Variance0.2456743033
MonotonicityNot monotonic
2021-11-29T11:26:08.520968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42951
 
7.3%
4.12654
 
6.6%
3.92631
 
6.5%
3.82549
 
6.3%
4.22118
 
5.3%
3.72100
 
5.2%
4.31794
 
4.4%
4.41588
 
3.9%
3.61489
 
3.7%
4.51238
 
3.1%
Other values (456)17357
43.0%
(Missing)1867
 
4.6%
ValueCountFrequency (%)
1.5751
 
< 0.1%
2.21
 
< 0.1%
2.252
 
< 0.1%
2.32
 
< 0.1%
2.351
 
< 0.1%
2.41
 
< 0.1%
2.59
< 0.1%
2.552
 
< 0.1%
2.610
< 0.1%
2.652
 
< 0.1%
ValueCountFrequency (%)
9.81
 
< 0.1%
9.42
< 0.1%
9.22
< 0.1%
91
 
< 0.1%
8.21
 
< 0.1%
7.91
 
< 0.1%
7.81
 
< 0.1%
7.63
< 0.1%
7.5951
 
< 0.1%
7.41
 
< 0.1%

Bilirubin_total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct450
Distinct (%)3.2%
Missing26088
Missing (%)64.7%
Infinite0
Infinite (%)0.0%
Mean1.45298112
Minimum0.1
Maximum49.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:08.616996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.5
median0.75
Q31.25
95-th percentile4.25
Maximum49.2
Range49.1
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation3.047215762
Coefficient of variation (CV)2.09721635
Kurtosis73.44687851
Mean1.45298112
Median Absolute Deviation (MAD)0.35
Skewness7.588950493
Sum20702.075
Variance9.2855239
MonotonicityNot monotonic
2021-11-29T11:26:08.714923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.51288
 
3.2%
0.61214
 
3.0%
0.41213
 
3.0%
0.71141
 
2.8%
0.8930
 
2.3%
0.3896
 
2.2%
0.9743
 
1.8%
1598
 
1.5%
0.2475
 
1.2%
1.1462
 
1.1%
Other values (440)5288
 
13.1%
(Missing)26088
64.7%
ValueCountFrequency (%)
0.197
 
0.2%
0.151
 
< 0.1%
0.1513
 
< 0.1%
0.2475
 
1.2%
0.2562
 
0.2%
0.3896
2.2%
0.322
 
0.1%
0.35100
 
0.2%
0.41213
3.0%
0.4251
 
< 0.1%
ValueCountFrequency (%)
49.21
< 0.1%
46.41
< 0.1%
45.91
< 0.1%
45.751
< 0.1%
44.91
< 0.1%
44.11
< 0.1%
43.31
< 0.1%
43.21
< 0.1%
42.351
< 0.1%
41.61
< 0.1%

TroponinI
Real number (ℝ≥0)

MISSING

Distinct1537
Distinct (%)21.8%
Missing33283
Missing (%)82.5%
Infinite0
Infinite (%)0.0%
Mean5.357604565
Minimum0.01
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:08.814567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.03
median0.095
Q31.195
95-th percentile33.342
Maximum440
Range439.99
Interquartile range (IQR)1.165

Descriptive statistics

Standard deviation19.02475329
Coefficient of variation (CV)3.550981237
Kurtosis89.08321688
Mean5.357604565
Median Absolute Deviation (MAD)0.085
Skewness7.707238255
Sum37787.185
Variance361.9412376
MonotonicityNot monotonic
2021-11-29T11:26:08.907571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.011238
 
3.1%
0.03805
 
2.0%
0.02290
 
0.7%
0.04256
 
0.6%
0.05176
 
0.4%
0.06144
 
0.4%
0.07134
 
0.3%
0.08100
 
0.2%
0.0995
 
0.2%
0.181
 
0.2%
Other values (1527)3734
 
9.3%
(Missing)33283
82.5%
ValueCountFrequency (%)
0.011238
3.1%
0.01552
 
0.1%
0.02290
 
0.7%
0.02533
 
0.1%
0.03805
2.0%
0.033
 
< 0.1%
0.0351
 
< 0.1%
0.03527
 
0.1%
0.04256
 
0.6%
0.04536
 
0.1%
ValueCountFrequency (%)
4401
 
< 0.1%
297.051
 
< 0.1%
20012
< 0.1%
199.531
 
< 0.1%
194.0951
 
< 0.1%
193.441
 
< 0.1%
190.041
 
< 0.1%
185.2151
 
< 0.1%
183.51
 
< 0.1%
180.081
 
< 0.1%

Hct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1042
Distinct (%)2.7%
Missing2317
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean32.05367001
Minimum9.3
Maximum66.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:09.004785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9.3
5-th percentile24
Q128.1
median31.5
Q335.6
95-th percentile41.7
Maximum66.4
Range57.1
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation5.44875831
Coefficient of variation (CV)0.1699885944
Kurtosis0.2783393642
Mean32.05367001
Median Absolute Deviation (MAD)3.7
Skewness0.4652011331
Sum1218648.48
Variance29.68896712
MonotonicityNot monotonic
2021-11-29T11:26:09.096131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29289
 
0.7%
31269
 
0.7%
30266
 
0.7%
32251
 
0.6%
28247
 
0.6%
30.5244
 
0.6%
35236
 
0.6%
31.5235
 
0.6%
31.1233
 
0.6%
29.6229
 
0.6%
Other values (1032)35520
88.1%
(Missing)2317
 
5.7%
ValueCountFrequency (%)
9.31
< 0.1%
10.151
< 0.1%
11.31
< 0.1%
121
< 0.1%
13.31
< 0.1%
13.551
< 0.1%
14.41
< 0.1%
14.551
< 0.1%
14.61
< 0.1%
15.51
< 0.1%
ValueCountFrequency (%)
66.41
< 0.1%
651
< 0.1%
64.21
< 0.1%
64.11
< 0.1%
62.451
< 0.1%
61.71
< 0.1%
61.051
< 0.1%
59.851
< 0.1%
59.61
< 0.1%
58.21
< 0.1%

Hgb
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct479
Distinct (%)1.3%
Missing2448
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean10.71732963
Minimum2.6
Maximum26.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:09.266727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile7.9
Q19.35
median10.55
Q311.95
95-th percentile14.1
Maximum26.6
Range24
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation1.903773429
Coefficient of variation (CV)0.1776350541
Kurtosis0.573395905
Mean10.71732963
Median Absolute Deviation (MAD)1.25
Skewness0.4943870074
Sum406058.185
Variance3.624353269
MonotonicityNot monotonic
2021-11-29T11:26:09.367500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.5650
 
1.6%
10629
 
1.6%
10.3625
 
1.5%
10.8619
 
1.5%
9.8602
 
1.5%
10.2597
 
1.5%
11.3594
 
1.5%
9.7584
 
1.4%
10.6581
 
1.4%
10.1580
 
1.4%
Other values (469)31827
78.9%
(Missing)2448
 
6.1%
ValueCountFrequency (%)
2.61
< 0.1%
3.31
< 0.1%
41
< 0.1%
4.151
< 0.1%
4.31
< 0.1%
4.51
< 0.1%
4.81
< 0.1%
4.91
< 0.1%
51
< 0.1%
5.11
< 0.1%
ValueCountFrequency (%)
26.61
< 0.1%
24.82
< 0.1%
23.81
< 0.1%
23.51
< 0.1%
22.51
< 0.1%
21.61
< 0.1%
21.21
< 0.1%
211
< 0.1%
20.61
< 0.1%
20.551
< 0.1%

PTT
Real number (ℝ≥0)

MISSING

Distinct1893
Distinct (%)9.4%
Missing20098
Missing (%)49.8%
Infinite0
Infinite (%)0.0%
Mean36.4915083
Minimum16.9
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:09.463725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum16.9
5-th percentile23.2
Q127.25
median31
Q337.6
95-th percentile69.7
Maximum250
Range233.1
Interquartile range (IQR)10.35

Descriptive statistics

Standard deviation19.22251501
Coefficient of variation (CV)0.5267667988
Kurtosis30.98766733
Mean36.4915083
Median Absolute Deviation (MAD)4.6
Skewness4.513585187
Sum738515.145
Variance369.5050834
MonotonicityNot monotonic
2021-11-29T11:26:09.560520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.6133
 
0.3%
28130
 
0.3%
28.9129
 
0.3%
28.1128
 
0.3%
29.5127
 
0.3%
27.7125
 
0.3%
27.6120
 
0.3%
26.5118
 
0.3%
28.5117
 
0.3%
28.7116
 
0.3%
Other values (1883)18995
47.1%
(Missing)20098
49.8%
ValueCountFrequency (%)
16.91
< 0.1%
17.11
< 0.1%
17.21
< 0.1%
17.31
< 0.1%
17.651
< 0.1%
18.11
< 0.1%
18.251
< 0.1%
18.42
< 0.1%
18.451
< 0.1%
18.52
< 0.1%
ValueCountFrequency (%)
2503
 
< 0.1%
249.95
 
< 0.1%
24916
< 0.1%
237.51
 
< 0.1%
235.51
 
< 0.1%
224.41
 
< 0.1%
212.31
 
< 0.1%
210.61
 
< 0.1%
2061
 
< 0.1%
204.91
 
< 0.1%

WBC
Real number (ℝ≥0)

MISSING

Distinct1148
Distinct (%)3.0%
Missing2625
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean11.0187729
Minimum0.1
Maximum319.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:09.658999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile4.6
Q17.55
median10.1
Q313.2
95-th percentile19.7
Maximum319.25
Range319.15
Interquartile range (IQR)5.65

Descriptive statistics

Standard deviation6.574039075
Coefficient of variation (CV)0.5966217047
Kurtosis360.6126
Mean11.0187729
Median Absolute Deviation (MAD)2.8
Skewness11.81150487
Sum415528.945
Variance43.21798975
MonotonicityNot monotonic
2021-11-29T11:26:09.751214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.8326
 
0.8%
8.8321
 
0.8%
10318
 
0.8%
10.3311
 
0.8%
10.2295
 
0.7%
9295
 
0.7%
8.6291
 
0.7%
8287
 
0.7%
7.4285
 
0.7%
8.5285
 
0.7%
Other values (1138)34697
86.0%
(Missing)2625
 
6.5%
ValueCountFrequency (%)
0.117
< 0.1%
0.151
 
< 0.1%
0.214
< 0.1%
0.251
 
< 0.1%
0.35
 
< 0.1%
0.31
 
< 0.1%
0.410
< 0.1%
0.452
 
< 0.1%
0.53
 
< 0.1%
0.551
 
< 0.1%
ValueCountFrequency (%)
319.251
< 0.1%
3021
< 0.1%
206.31
< 0.1%
200.81
< 0.1%
179.21
< 0.1%
168.61
< 0.1%
165.11
< 0.1%
160.61
< 0.1%
152.91
< 0.1%
150.41
< 0.1%

Fibrinogen
Real number (ℝ≥0)

MISSING

Distinct987
Distinct (%)21.9%
Missing35821
Missing (%)88.8%
Infinite0
Infinite (%)0.0%
Mean303.3470543
Minimum35
Maximum1383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:09.848732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile129
Q1196
median260.5
Q3366.75
95-th percentile634
Maximum1383
Range1348
Interquartile range (IQR)170.75

Descriptive statistics

Standard deviation158.1892501
Coefficient of variation (CV)0.5214794337
Kurtosis3.158885017
Mean303.3470543
Median Absolute Deviation (MAD)78.5
Skewness1.551861637
Sum1369611.95
Variance25023.83884
MonotonicityNot monotonic
2021-11-29T11:26:09.949905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21732
 
0.1%
21424
 
0.1%
21524
 
0.1%
20224
 
0.1%
24823
 
0.1%
18323
 
0.1%
20622
 
0.1%
15420
 
< 0.1%
22320
 
< 0.1%
20320
 
< 0.1%
Other values (977)4283
 
10.6%
(Missing)35821
88.8%
ValueCountFrequency (%)
351
< 0.1%
52.51
< 0.1%
55.51
< 0.1%
56.51
< 0.1%
581
< 0.1%
59.51
< 0.1%
601
< 0.1%
612
< 0.1%
61.51
< 0.1%
621
< 0.1%
ValueCountFrequency (%)
13831
 
< 0.1%
12461
 
< 0.1%
12111
 
< 0.1%
11611
 
< 0.1%
10511
 
< 0.1%
10301
 
< 0.1%
10006
< 0.1%
9761
 
< 0.1%
9601
 
< 0.1%
9561
 
< 0.1%

Platelets
Real number (ℝ≥0)

MISSING

Distinct1344
Distinct (%)3.6%
Missing2577
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean206.308925
Minimum3
Maximum2322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:10.050263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile81
Q1141
median191
Q3251.75
95-th percentile381
Maximum2322
Range2319
Interquartile range (IQR)110.75

Descriptive statistics

Standard deviation99.95600509
Coefficient of variation (CV)0.4844967569
Kurtosis13.79033412
Mean206.308925
Median Absolute Deviation (MAD)54.5
Skewness2.030743645
Sum7790018.7
Variance9991.202954
MonotonicityNot monotonic
2021-11-29T11:26:10.143382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
186171
 
0.4%
158167
 
0.4%
180166
 
0.4%
184166
 
0.4%
167165
 
0.4%
163163
 
0.4%
187163
 
0.4%
206161
 
0.4%
182155
 
0.4%
185155
 
0.4%
Other values (1334)36127
89.6%
(Missing)2577
 
6.4%
ValueCountFrequency (%)
31
 
< 0.1%
42
< 0.1%
4.52
< 0.1%
52
< 0.1%
5.51
 
< 0.1%
72
< 0.1%
7.51
 
< 0.1%
82
< 0.1%
8.51
 
< 0.1%
93
< 0.1%
ValueCountFrequency (%)
23221
< 0.1%
1687.51
< 0.1%
15441
< 0.1%
13431
< 0.1%
1300.51
< 0.1%
11911
< 0.1%
1167.51
< 0.1%
1125.51
< 0.1%
1096.51
< 0.1%
1067.51
< 0.1%

Age
Real number (ℝ≥0)

Distinct5987
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.64342324
Minimum14
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:10.239813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile30
Q151
median63.11
Q374
95-th percentile85
Maximum100
Range86
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.48294561
Coefficient of variation (CV)0.2673917954
Kurtosis-0.2334728394
Mean61.64342324
Median Absolute Deviation (MAD)11.27
Skewness-0.4250999292
Sum2486449.12
Variance271.6874961
MonotonicityNot monotonic
2021-11-29T11:26:10.335706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67581
 
1.4%
68545
 
1.4%
66521
 
1.3%
65512
 
1.3%
61502
 
1.2%
69498
 
1.2%
71490
 
1.2%
62480
 
1.2%
70478
 
1.2%
63473
 
1.2%
Other values (5977)35256
87.4%
ValueCountFrequency (%)
142
 
< 0.1%
152
 
< 0.1%
165
 
< 0.1%
1713
< 0.1%
1832
0.1%
18.113
 
< 0.1%
18.131
 
< 0.1%
18.142
 
< 0.1%
18.151
 
< 0.1%
18.181
 
< 0.1%
ValueCountFrequency (%)
100392
1.0%
89112
 
0.3%
88.991
 
< 0.1%
88.982
 
< 0.1%
88.974
 
< 0.1%
88.961
 
< 0.1%
88.954
 
< 0.1%
88.942
 
< 0.1%
88.931
 
< 0.1%
88.925
 
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size315.2 KiB
1.0
22566 
0.0
17770 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters121008
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.022566
55.9%
0.017770
44.1%

Length

2021-11-29T11:26:10.428718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:26:10.481409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.022566
55.9%
0.017770
44.1%

Most occurring characters

ValueCountFrequency (%)
058106
48.0%
.40336
33.3%
122566
 
18.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number80672
66.7%
Other Punctuation40336
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
058106
72.0%
122566
 
28.0%
Other Punctuation
ValueCountFrequency (%)
.40336
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common121008
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
058106
48.0%
.40336
33.3%
122566
 
18.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII121008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
058106
48.0%
.40336
33.3%
122566
 
18.6%

Unit1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing15617
Missing (%)38.7%
Memory size315.2 KiB
0.0
12452 
1.0
12267 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters74157
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.012452
30.9%
1.012267
30.4%
(Missing)15617
38.7%

Length

2021-11-29T11:26:10.533198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:26:10.656569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.012452
50.4%
1.012267
49.6%

Most occurring characters

ValueCountFrequency (%)
037171
50.1%
.24719
33.3%
112267
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number49438
66.7%
Other Punctuation24719
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
037171
75.2%
112267
 
24.8%
Other Punctuation
ValueCountFrequency (%)
.24719
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common74157
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
037171
50.1%
.24719
33.3%
112267
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII74157
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
037171
50.1%
.24719
33.3%
112267
 
16.5%

Unit2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing15617
Missing (%)38.7%
Memory size315.2 KiB
1.0
12452 
0.0
12267 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters74157
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.012452
30.9%
0.012267
30.4%
(Missing)15617
38.7%

Length

2021-11-29T11:26:10.708523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:26:10.757418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.012452
50.4%
0.012267
49.6%

Most occurring characters

ValueCountFrequency (%)
036986
49.9%
.24719
33.3%
112452
 
16.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number49438
66.7%
Other Punctuation24719
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
036986
74.8%
112452
 
25.2%
Other Punctuation
ValueCountFrequency (%)
.24719
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common74157
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
036986
49.9%
.24719
33.3%
112452
 
16.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII74157
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
036986
49.9%
.24719
33.3%
112452
 
16.8%

HospAdmTime
Real number (ℝ)

ZEROS

Distinct12156
Distinct (%)30.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-51.84894508
Minimum-5366.86
Maximum23.99
Zeros1313
Zeros (%)3.3%
Negative38767
Negative (%)96.1%
Memory size315.2 KiB
2021-11-29T11:26:10.819754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-5366.86
5-th percentile-240.942
Q1-43.685
median-6.05
Q3-0.04
95-th percentile-0.01
Maximum23.99
Range5390.85
Interquartile range (IQR)43.645

Descriptive statistics

Standard deviation139.766452
Coefficient of variation (CV)-2.695646975
Kurtosis175.3735088
Mean-51.84894508
Median Absolute Deviation (MAD)6.03
Skewness-9.578944604
Sum-2091327.2
Variance19534.6611
MonotonicityNot monotonic
2021-11-29T11:26:10.922251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.023999
 
9.9%
-0.032487
 
6.2%
01313
 
3.3%
-0.011293
 
3.2%
-0.04794
 
2.0%
-0.05436
 
1.1%
-0.06241
 
0.6%
-0.07176
 
0.4%
-0.09108
 
0.3%
-0.0899
 
0.2%
Other values (12146)29389
72.9%
ValueCountFrequency (%)
-5366.861
< 0.1%
-3710.661
< 0.1%
-3397.641
< 0.1%
-3342.341
< 0.1%
-3322.91
< 0.1%
-3269.11
< 0.1%
-3212.561
< 0.1%
-3189.391
< 0.1%
-3141.551
< 0.1%
-3112.121
< 0.1%
ValueCountFrequency (%)
23.991
< 0.1%
22.041
< 0.1%
20.041
< 0.1%
17.341
< 0.1%
16.021
< 0.1%
14.651
< 0.1%
14.211
< 0.1%
141
< 0.1%
11.941
< 0.1%
10.991
< 0.1%

ICULOS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct276
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.26911444
Minimum4.5
Maximum320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:11.023851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile8
Q113
median20
Q324.5
95-th percentile30
Maximum320
Range315.5
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation11.69589542
Coefficient of variation (CV)0.57703041
Kurtosis64.42858314
Mean20.26911444
Median Absolute Deviation (MAD)5.5
Skewness5.556797402
Sum817575
Variance136.7939696
MonotonicityNot monotonic
2021-11-29T11:26:11.118972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201273
 
3.2%
20.51248
 
3.1%
211241
 
3.1%
19.51233
 
3.1%
221227
 
3.0%
21.51220
 
3.0%
22.51149
 
2.8%
18.51148
 
2.8%
231120
 
2.8%
191113
 
2.8%
Other values (266)28364
70.3%
ValueCountFrequency (%)
4.5281
0.7%
5189
 
0.5%
5.5189
 
0.5%
6192
 
0.5%
6.5236
 
0.6%
7286
0.7%
7.5351
0.9%
8451
1.1%
8.5531
1.3%
9636
1.6%
ValueCountFrequency (%)
3201
 
< 0.1%
3091
 
< 0.1%
2861
 
< 0.1%
1701
 
< 0.1%
169.51
 
< 0.1%
1692
 
< 0.1%
168.510
< 0.1%
1681
 
< 0.1%
167.51
 
< 0.1%
165.51
 
< 0.1%

SepsisLabel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size315.2 KiB
0.0
39392 
1.0
 
912
0.5
 
32

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters121008
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.039392
97.7%
1.0912
 
2.3%
0.532
 
0.1%

Length

2021-11-29T11:26:11.212873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:26:11.266514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.039392
97.7%
1.0912
 
2.3%
0.532
 
0.1%

Most occurring characters

ValueCountFrequency (%)
079728
65.9%
.40336
33.3%
1912
 
0.8%
532
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number80672
66.7%
Other Punctuation40336
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
079728
98.8%
1912
 
1.1%
532
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.40336
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common121008
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
079728
65.9%
.40336
33.3%
1912
 
0.8%
532
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII121008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
079728
65.9%
.40336
33.3%
1912
 
0.8%
532
 
< 0.1%

Sepsis
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size315.2 KiB
0.0
37404 
1.0
 
2932

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters121008
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.037404
92.7%
1.02932
 
7.3%

Length

2021-11-29T11:26:11.326874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:26:11.379703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.037404
92.7%
1.02932
 
7.3%

Most occurring characters

ValueCountFrequency (%)
077740
64.2%
.40336
33.3%
12932
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number80672
66.7%
Other Punctuation40336
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
077740
96.4%
12932
 
3.6%
Other Punctuation
ValueCountFrequency (%)
.40336
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common121008
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
077740
64.2%
.40336
33.3%
12932
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII121008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
077740
64.2%
.40336
33.3%
12932
 
2.4%

Hours
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct273
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.48200119
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size315.2 KiB
2021-11-29T11:26:11.442522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q124
median38
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)23

Descriptive statistics

Standard deviation22.79592332
Coefficient of variation (CV)0.5923788425
Kurtosis42.42147962
Mean38.48200119
Median Absolute Deviation (MAD)11
Skewness4.840449261
Sum1552210
Variance519.6541201
MonotonicityNot monotonic
2021-11-29T11:26:11.540473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
361364
 
3.4%
391362
 
3.4%
381323
 
3.3%
401285
 
3.2%
411271
 
3.2%
371223
 
3.0%
431210
 
3.0%
421193
 
3.0%
441143
 
2.8%
451089
 
2.7%
Other values (263)27873
69.1%
ValueCountFrequency (%)
8328
0.8%
9236
 
0.6%
10221
 
0.5%
11234
 
0.6%
12285
 
0.7%
13350
0.9%
14412
1.0%
15536
1.3%
16601
1.5%
17721
1.8%
ValueCountFrequency (%)
33610
< 0.1%
3353
 
< 0.1%
3342
 
< 0.1%
3331
 
< 0.1%
3301
 
< 0.1%
3281
 
< 0.1%
3271
 
< 0.1%
3261
 
< 0.1%
3201
 
< 0.1%
3181
 
< 0.1%

Interactions

2021-11-29T11:25:59.734202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:56.225195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:56.321392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:56.486423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:56.579492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:56.666365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:56.759516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:56.849536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:56.938085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:57.022412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:57.106606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:57.192112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:57.276065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:57.360948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:57.447604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:57.531630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:57.621096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:57.708168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:57.791807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:57.886561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:57.972756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:58.066156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:58.151403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:58.244867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:58.335392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:58.422819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:58.586857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:58.678674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:58.763395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:58.849800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:58.937360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:59.025440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:59.109555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:59.196373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:59.282993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:59.377186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:59.465423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:59.551479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:25:59.643196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-29T11:26:11.685384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-29T11:26:12.019800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-29T11:26:12.428477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-29T11:26:12.705317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-29T11:25:59.972509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-29T11:26:01.059606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-29T11:26:01.937162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-29T11:26:02.763054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
01104.091.036.725128.0087.915NaN25.000NaN20.046.50.297.34098.088.516.018.098.09.4585.00.70NaN163.0NaN2.103.504.20.3NaN36.7012.35NaN10.2NaN327.583.140.0NaNNaN-0.0327.50.00.054.0
1260.097.036.110133.0065.00043.0012.000NaNNaN22.0NaNNaNNaNNaNNaN100.0NaN7.90113.02.50NaN78.0NaN2.504.405.1NaNNaN27.809.70NaN11.0NaN158.075.910.00.01.0-98.6012.00.00.023.0
2381.095.037.585140.0081.00053.5026.000NaN6.531.00.607.49039.5NaNNaN30.0NaN11.0099.00.90NaN113.0NaN2.402.403.8NaNNaN28.159.1030.08.7NaN486.045.820.01.00.0-1195.7124.50.00.048.0
34105.098.036.390114.5067.83550.2518.000NaN0.022.0NaN7.40043.098.0NaN16.5NaN8.20106.50.80NaN84.0NaN2.053.804.3NaNNaN25.808.3021.87.6NaN182.065.710.00.01.0-8.7715.00.00.029.0
4573.597.037.220133.5087.000NaN16.000NaNNaN25.0NaNNaNNaNNaN16.07.065.08.20105.00.60NaN128.0NaN2.202.803.60.6NaN41.0014.4029.08.0NaN276.028.091.01.00.0-0.0525.50.00.048.0
56100.598.536.670122.0089.000NaN24.000NaN0.029.00.407.34047.0NaNNaN9.0NaNNaN111.00.70NaN73.01.4NaNNaN3.8NaNNaN36.9012.20NaN12.0NaN298.052.011.01.00.0-0.0311.00.00.017.0
67121.095.037.885109.0074.50060.0021.000NaN-9.016.00.407.33027.0NaN452.066.088.07.35115.53.85NaN193.52.21.751.303.11.4NaN41.8515.3026.39.3NaN45.064.241.01.00.0-0.0523.00.00.045.0
7875.0100.036.220108.7565.00048.5016.750NaN-8.015.0NaN7.35527.0NaNNaN29.0NaN8.00106.01.20NaN108.01.61.903.504.9NaNNaN25.908.90NaN9.4NaN213.087.081.0NaNNaN-2.2320.50.00.040.0
89113.098.037.720120.0080.00065.0021.875NaN0.027.00.407.37048.097.0NaN19.0NaN8.00107.00.90NaN119.52.22.053.053.7NaNNaN27.409.6030.910.9232.5175.027.921.0NaNNaN-0.03129.50.01.0258.0
91079.096.037.000112.0072.00054.0019.000NaN0.024.00.407.40537.597.0NaN17.5NaNNaN106.01.00NaN107.01.12.10NaN3.9NaNNaN31.9010.2029.99.3NaN111.076.710.00.01.0-2.3614.00.00.023.0

Last rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
4032611999176.097.037.00130.0082.052.016.035.0NaNNaNNaNNaNNaNNaNNaN14.0NaN7.50NaN0.790NaN110.00NaN1.60NaN4.10NaNNaN26.308.40NaN6.4NaN96.081.00.00.01.0-66.1313.00.00.025.0
4032711999291.096.036.80144.50100.073.020.0NaNNaNNaNNaNNaNNaNNaNNaN37.0NaN9.20NaN9.930NaN103.00NaN1.806.64.50NaN0.4130.209.50NaN2.8NaN198.045.01.01.00.0-4.5521.00.00.041.0
4032811999381.095.036.50130.0097.077.519.0NaNNaNNaNNaNNaNNaNNaNNaN15.0NaN8.30NaN1.010NaN132.00NaN2.002.64.10NaN0.0142.0014.90NaN12.3NaN175.065.01.0NaNNaN-3.5311.00.00.021.0
4032911999474.096.037.50122.0076.056.018.028.0NaNNaN0.47.29534.598.55NaN16.0NaN1.26NaN1.060NaN135.506.662.003.64.05NaNNaN30.3010.25NaN9.3NaN66.571.01.00.01.0-29.5721.50.00.042.0
4033011999562.095.036.05147.00107.079.020.0NaNNaNNaNNaNNaNNaNNaNNaN9.0NaN8.80NaN0.810NaN116.00NaN2.003.03.50NaNNaN39.2013.10NaN7.0289.0154.076.01.00.01.0-14.9021.50.00.042.0
4033111999685.598.036.50130.0085.072.019.0NaNNaNNaNNaNNaNNaNNaN849.06.0259.08.75NaN0.495NaN147.50NaN1.95NaN3.653.30.0142.7013.80NaN12.6NaN238.084.00.0NaNNaN-6.6924.50.00.048.0
4033211999761.597.036.80117.5086.069.022.045.0NaNNaNNaNNaNNaNNaN24.05.5116.013.80NaN0.7700.1103.75NaN3.153.13.350.71.0946.7015.5538.210.4NaN189.030.01.0NaNNaN-0.0213.00.00.025.0
4033311999881.598.036.70155.75114.087.021.0NaNNaNNaNNaNNaNNaNNaN9.055.068.08.05NaN7.685NaN87.00NaN1.904.14.250.2NaN27.608.20NaN12.5NaN188.060.00.01.00.0-53.6425.00.00.049.0
4033411999994.093.037.60140.00100.075.022.0NaNNaNNaNNaNNaNNaNNaN33.528.549.58.45NaN1.010NaN109.50NaNNaNNaN3.401.0NaN23.608.00NaN10.7NaN263.584.00.01.00.0-10.7410.50.00.020.0
4033512000082.098.036.70123.0090.070.016.0NaNNaNNaNNaNNaNNaNNaN18.010.075.09.10NaN0.5350.1192.00NaN2.204.03.450.9NaN37.3511.8029.16.1NaN223.062.00.0NaNNaN0.0018.00.00.035.0